Radiotherapy (also called Radiation therapy) is a cancer treatment that uses high doses of radiation to destroy cancer cells and shrink tumors. Into external radiotherapy, there is the Intensity Modulated Radiation Therapy (known as IMRT), where it is taken a specific part of the body through the deliver the dose from different angles to damage the tumor, avoiding surrounding organs.When IMRT is approached as a sequential problem, we first need to establish a set of beam angles from which radiation will be released then, the radiation intensities for each selected beam angles are computed. Finally, the sequence of apertures we need to deliver the computed treatment plan is generated. Unlike this sequential approach, in the Direct Aperture Optimization (DAO) problems, constraints associated with the number of deliverable aperture shapes, just as some physical constraints, are taken into consideration while the intensities optimisation process is taking place. According to some authors, DAO generates better treatments with fewer apertures for IMRT.In this work, we propose a heuristic algorithm, mixing a local search algorithm and mathematical programming to solve the DAO problem. We apply our algorithm on a prostate cancer case and compare ours results with those obtained in the sequential approach. Results show that our algorithms can find treatment plans in competitive time when considering the number of deliverable aperture shapes.